Searching for sex- and gender-sensitive tuberculosis research in public health: finding a needle in a haystack
Bibliographic record
Abstract
Bilkis Vissandjee,1 Assia Mourid,2 Christina A Greenaway,3 Wendy E Short,4 Jodi A Proctor5 1Faculty of Nursing, Public Health Research Institute, Université de Montréal, Montréal, Montréal, QC, Canada; 2Allied Health Library, Université de Montréal, Montréal, QC, Canada; 3Department of Medicine, McGill University, Division of Infectious Diseases, Jewish General Hospital, Montréal, QC, Canada; 4Faculty of Humanities and Social Sciences, School of Social Sciences, University of Queensland, St Lucia, QLD, Australia; 5School of Social Work, McGill University, Montréal, QC, Canada Abstract: Despite broadening consideration of sex- and gender-based issues in health research, when seeking information on how sex and gender contribute to disease contexts for specific health or public health topics, a lack of consistent or systematic use of terminology in health literature means that it remains difficult to identify research with a sex or gender focus. These inconsistencies are driven, in part, by the complexity and terminological inflexibility of the indexing systems for gender- and sex-related terms in public health databases. Compounding the issue are authors’ diverse vocabularies, and in some cases lack of accuracy in defining and using fundamental sex–gender terms in writing, and when establishing keyword lists and search criteria. Considering the specific case of the tuberculosis (TB) prevention and management literature, an analysis of sex and gender sensitivity in three health databases was performed. While there is an expanding literature exploring the roles of both sex and gender in the trajectory and lived experience of TB, we demonstrate the potential to miss relevant research when attempting to retrieve literature using only the search criteria currently available. We, therefore, argue that for good clinical practice to be achieved; there is a need for both public health researchers and users to be better educated in appropriate usage of the terminology associated with sex and gender. In addition, public health database indexers ought to accept the task of developing and implementing adequate definitions of sex and gender terms so as to facilitate access to sex- and gender-related research. These twin advances will allow clinicians to more readily recognize and access knowledge pertaining to systems of redress that respond to gendered risks that compound existing health inequalities in disease management and control, particularly when dealing with already complex diseases. Given the methodological and linguistic challenges presented by the multidimensional and highly contextual nature of definitions of sex and gender, it will be important that this review task be undertaken using a multidisciplinary approach. Keywords: sex, gender, tuberculosis, literature search, indexing, databases, terminological accuracy, keywords search
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.325 | 0.584 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.063 | 0.050 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.040 | 0.070 |
| Open science | 0.007 | 0.029 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".